Computational tool choice impacts CRISPR spacer-protospacer detection.

Area of Science:

• Microbiology
• Viromics
• Bioinformatics

Background:

• CRISPR spacer-protospacer matching is crucial for inferring host-virus interactions.
• Current sequence search tools are often under-evaluated for this specific application.

Purpose of the Study:

• To benchmark commonly used sequence search and alignment tools for CRISPR spacer-protospacer matching.
• To evaluate tool performance across various datasets and reporting behaviors.

Main Methods:

• Utilized synthetic, semi-synthetic, and real-world datasets for benchmarking.
• Assessed tools based on recall, runtime, and resource usage across different distance metrics and thresholds.

Main Results:

• Observed substantial performance differences among commonly used tools.
• Identified significant variations in recall, runtime, and resource consumption.
• Provided practical default settings for large-scale spacer-target matching.

Conclusions:

• The choice of tool and its configuration significantly impacts the accuracy of host-virus interaction inference.
• Clarified trade-offs between exhaustive and heuristic approaches for spacer-target matching.
• Recommendations are provided for optimizing CRISPR-based microbial and viromics studies.